首页 | 官方网站   微博 | 高级检索  
     

支持向量机在模式识别中的应用
引用本文:沈明华,肖立,王飞行.支持向量机在模式识别中的应用[J].电讯技术,2006,46(4):9-12.
作者姓名:沈明华  肖立  王飞行
作者单位:国防科技大学,电子科学与工程学院,长沙,410073
摘    要:针对传统神经网络存在网络结构难于确定、过学习以及局部极小等问题,研究了基于支持向量机(SVM)的模式识别问题。通过对棋盘这种典型非线性二值问题的分类研究,分析了支持向量机的分类与泛化能力。支持向量机在分类和泛化能力方面远远优于传统神经网络。最后将支持向量机用于对两类飞机目标的分类识别,通过多组蒙特卡罗试验,获得了较好的识别结果。支持向量机在目标识别中有巨大潜力和广阔前景。

关 键 词:模式识别  支持向量机  径向基函数  泛化能力  目标识别
文章编号:1001-893X(2006)04-0009-04
收稿时间:2005-12-14
修稿时间:2005-12-142006-04-04

Application of Support Vector Machine(SVM) in Pattern Recognition
SHEN Ming-hu,XIAO Li,WANG Fei-xing.Application of Support Vector Machine(SVM) in Pattern Recognition[J].Telecommunication Engineering,2006,46(4):9-12.
Authors:SHEN Ming-hu  XIAO Li  WANG Fei-xing
Affiliation:School of Electronic Science and Engineering, National University of Defence Technology, Changsha 410073 ,China
Abstract:Aiming at the problems such as difficult determination of net structure,over fitting and local minimization of traditional neural networks,the support vector machine(SVM) applied to pattern recognition is studied.By investigating the chessboard classification,which is typical of nonlinear two-value problem,the generalization ability of SVM is analyzed. SVM is more powerful than traditional neural network in the aspect of classification and generalization.Finally two kinds of airplanes are recognized based on SVM,with many Monte-Carlo experiments good classification results are achieved.SVM has huge potentials and good prospect in the area of target recognition.
Keywords:pattern recognition  support vector machine(SVM)  range profiles  generalization ability  target recognition
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《电讯技术》浏览原始摘要信息
点击此处可从《电讯技术》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号